Powering Consistent, High-throughput, Real-time Distributed Calculation Engines Using Kafka Streams with Kamlesh Shah
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"Building financial-grade applications involve performing complex calculations over a wide range of data from across different domains, with challenges including stringent accuracy requirements, latency constraints, along with the need to share states across distributed services. During this session, I will cover how, at Morgan Stanley, we built a real-time, microservices based Liquidity Management platform using event streaming with Kafka Streams API, to tackle high volumes of data and to perform calculations on cross domain events, spanning wide time windows over the past and the future. I will demonstrate how we used Kafka Streams & state stores, along with patterns like Saga to achieve eventual data consistency and use state-enriched events to decouple services when transferring them through multiple business domains. I will cover mechanisms to ensure accuracy and transparency with idempotency at heart along with error detection and replay strategies. Finally, I will look at how we used a high-performant in-memory cache to stage the results of cascaded KStream based calculation engines, which powered our high-speed, ticking and stateful data visualisations."
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Powering Consistent, High-throughput, Real-time Distributed Calculation Engines Using Kafka Streams with Kamlesh Shah
- 1. Powering consistent, high-throughput, real-time distributed calculation engines using Kafka Streams Kamlesh Shah
- 2. Intro Kamlesh Shah – Technical Architect Morgan Stanley Kafka Summit 2023, London
- 3. KAFKA SUMMIT 2023 3 Agenda Topics Covered § Problem statement § Distributed processing • Kafka for data ingestion & distribution • Stream processing § Example distributed application • Kafka stream & State store usage • Stateful & Cascading Calculators • Idempotency • Joining static & not-so-static data • Eventual Consistency with Saga § State-Enriched Events & Decoupling of Services § Time windows and Replay § Architecture for Real-time Ticking Stateful Views § Summary
- 4. KAFKA SUMMIT 2023 4 Problem statement Transactions Market Data Legacy Systems Business Events Calc.. Risk Calc.. Calc.. Calc.. Regulation Fraud Screen Limits § Data of different types and domains § Internal & External § Distributed processing § Coupling of services § Hard to change § Infrastructure complexity § Scaling § Fault identification § Calculations § Timely § Accurate & consistent § Realtime User views § Critical decision making
- 5. KAFKA SUMMIT 2023 5 Kafka Data Ingestion & Distribution Transactions Market Data Legacy Systems Business Events Input Streams Stream Processing Kafka stream Analytics & Reporting Data Store Cache API Kafka stream Kafka stream Kafka stream Distributed Processing with Kafka – a stream processing use case
- 6. KAFKA SUMMIT 2023 6 Example distributed application Client 1 Client N Client 2 Account 1 Account 2 Account N Limit Transaction Transaction Transaction Transaction Transaction Account Now Time 2 … Time N Account 1 Account 2 … Account N Client Now Time 2 … Time N Client 1 nnn xxx Client 2 … Client N Future Balance Business case overview Current Balance
- 7. KAFKA SUMMIT 2023 7 Example distributed application Transaction Transaction Id = 1 Account Id = aa1 Debit or Credit = Debit Amount = 100 Date = T+1 Transaction Transaction Id = 2 Account Id = aa1 Debit or Credit = Debit Amount = 200 Date = T+1 Transaction Transaction Id = 3 Account Id = aa1 Debit or Credit = Debit Amount = 100 Date = T+2 .. Account Balance Service Account Balance Account Id = aa1 Projected Balance = 200 Date = T+1 Account Balance Account Id = aa1 Projected Balance = 100 Date = T+2 Transactions to Account Balances
- 8. KAFKA SUMMIT 2023 8 Account Balance Service Cash Transactions Topic Account Balances Topic .. .. .. .. Account Balance Account ID .. .. Date Projected Balance .. Transaction Transaction Id Account Id .. Debit or Credit Date Amount .. .. .. .. .. Consumer 1 Stateful Processor State Store streaming sources Kafka stream Stateful Account Balance Calculator
- 9. KAFKA SUMMIT 2023 9 Account Balance Calculator – Deep Dive final StreamsBuilder builder = new StreamsBuilder(); final KTable<TransactionId, Transaction> transactionTable = builder.table(transactionsTopic, Materialized…) ); final KGroupedTable<AccountId, Transaction> transactionByAccount = transactionTable.groupBy((k,v) -> KeyValue.pair(v.accountId, v), Grouped.with(…)); final KTable<AccountId, AccountBalance> accountBalanceKTable = transactionByAccount.aggregate(()-> createNewAccountBalance(), (key, value, aggregate) -> addAccountBalance(value, aggregate), (key, value, aggregate) -> removeAccountBalance(value, aggregate), Materialized.with(Serdes.String(), new JSONSerde<>()) ); accountBalanceKTable.toStream().to(accountBalancesTopic);
- 10. KAFKA SUMMIT 2023 10 Cash Transactions Topic Account Balances Topic Account Balance Account ID .. .. Projected Balance .. Transaction Transaction Id Account Id .. Debit or Credit Amount .. streaming sources Account Balance Service Consumer N Stateful Processor State Store Account Balance Service Consumer 1 Stateful Processor State Store .. .. .. .. .. .. .. .. Partition 1 Partition N .. .. .. .. .. .. .. .. Partition 1 Partition N Kafka stream Kafka stream Scale Number of Consumers <-> Number of Partitions Stateful Calculations with Kafka Stream – Scaling Consumers Partition Key Partition Key
- 11. KAFKA SUMMIT 2023 11 Client Balance Service Account Balance Account Id = aa1 Projected Balance = 200 Date = T+1 Account Balance Account Id = aa1 Projected Balance = 100 Date = T+2 Client To Account Map Client Id = 123 Account List [aa1, aa2…] Client Balance Client Id = 123 Projected Balance = 200 Date = T+1 Client Balance Client Id = 123 Projected Balance = 100 Date = T+2 Account Balances to Client Balances
- 12. KAFKA SUMMIT 2023 12 Cash Transactions Topic Account Balance Service Account Balances Topic Client Balance Service Client Balances Topic API Client to Account Reference Client to Account Cache .. .. .. .. .. .. .. .. Account Balance Account ID .. .. Projected Balance .. Client Balance Client ID .. .. Projected Balance .. Client Balances calculation with reference data Cascading Calculators
- 13. KAFKA SUMMIT 2023 13 Cash Transactions Topic Account Balance Service Account Balances Topic Client Balance Service Client Balances Topic API Client to Account Reference Client to Account Cache .. .. .. .. Client Balance Client ID .. .. Projected Balance .. Client Limits Topic streaming sources .. .. .. .. Client Limit Monitor Service .. .. .. .. Limit Breach Topic Client Limit Client ID .. .. Limit .. Client Limit Breach Client ID Projected Balance Limit Breach Y/N.. Cascading Calculators Client Limits using Kafka Join
- 14. KAFKA SUMMIT 2023 14 Client Limits Topic .. .. .. .. Client Limit Monitor Service .. .. .. .. Limit Breach Topic Client Limit Client ID .. .. Limit .. Client Limit Breach Client ID Projected Balance Limit Breach Y/N.. Client Balances Topic .. .. .. .. Client Balance Client ID .. .. Projected Balance .. Time Client Projected Balance Limit Breach Y/N T0 C1 190 200 N T1 C1 190 180 Y … … Tn C2 150 200 N Time Client Limit T0 C1 200 T1 C1 180 … … Tn C2 200 Time Client Projected Balance T0 C1 190 T1 C1 190 … … Tn C2 150 Kafka Join Kafka stream Kafka stream Kafka stream Cascading Calculators Client Limits – Breach
- 15. KAFKA SUMMIT 2023 15 15 Cascading Calculations – Kafka Join Kafka Join Choice Matching Key => Client ID Client Balances Topic Client Limit Topic Ktable Client Balance Ktable Client Limit Time Client ID Client Balance Limit Balance, Breach 1 1 10 15 10,Y 2 2 -10 -10, 3 2 -20 15 -20,Y 4 2 30 15 30,N 5 2 40 30,Y 6 3 100 7 3 40 40,Y .. n
- 16. KAFKA SUMMIT 2023 16 Account Balance Service Cash Transactions Topic Account Balances Topic .. .. .. .. Account Balance Account ID = A .. .. Projected Balance = 10 Transaction Transaction Id = 1 Account Id = A .. Debit or Credit = C Amount = 10 .. .. .. .. .. Transaction Transaction Id = 2 Account Id = A .. Debit or Credit = D Amount = 25 .. Transaction Transaction Id = 1 Account Id = A .. Debit or Credit = C Amount = 10 .. Account Balance Account ID = A .. .. Projected Balance = 35 Handling duplicate events Kafka stream Idempotency
- 17. KAFKA SUMMIT 2023 17 Deep Dive State Store Handling duplicate events Transaction Transaction Id = 1 .. Transaction Transaction Id = 1 .. Does TransactionID exist in SS? Add Balance Remove Balance Yes No Add Balance Amount = 100 Account Bal = 100 Amount = 100 Account Bal = 0 Account Bal = 100 final KTable<AccountId, AccountBalance> accountBalanceKTable = transactionByAccount.aggregate(()-> createNewAccountBalance(), (key, value, aggregate) -> addAccountBalance(value, aggregate), (key, value, aggregate) -> removeAccountBalance(value, aggregate), Materialized.with(Serdes.String(), new JSONSerde<>()) );
- 18. KAFKA SUMMIT 2023 18 Cash Transactions Account Balances Account Balance Service Client Balances Transaction Transaction ID Amount = 10 Eventual Consistency Saga – Rollback transactions Client Balance Service Update Account Balance Account Balance Account ID Transaction ID Balance += 10 Update Client Balance Client Balance Client ID Account ID Transaction ID Cancel Transaction Transaction Transaction ID Cancel = Y Rollback Account Balance Account Balance Account ID Transaction ID Balance -= 10 Error Update Client Balance Client Balance Client ID Account ID Transaction ID Eventually Consistent Success
- 19. KAFKA SUMMIT 2023 19 Account Balance Service Cash Transactions Topic Account Balances Topic .. .. .. .. Account Balance Account ID = A .. .. Projected Balance = 10 Transaction Instruction Id = 1 Account Id = A .. Debit or Credit = C Amount = 10 .. .. .. .. .. Transaction Instruction Id = 2 Account Id = A .. Debit or Credit = D Amount = 25 .. Transaction Instruction Id = 1 Account Id = A .. Debit or Credit = C Amount = 10 Cancel = Yes .. Account Balance Account ID = A .. .. Projected Balance = 35 Account Balance Account ID = A .. .. Projected Balance = 25 Handling Cancellations/Rollbacks Kafka stream
- 20. KAFKA SUMMIT 2023 20 Deep Dive KTable<AccountId, AccountBalance> accBalAggregate = groupedTable.aggregate(AccountBalance::getNewInstance, new AddBalanceAggregator(), new RemoveBalanceAggregator(), Materialized…) State Store Handling Cancels Transaction Transaction Id = 1 Cancel = Yes .. Transaction Transaction Id = 1 .. Does TransactionID exist in SS? Add Balance Remove Balance Yes No Add Balance (If Cancel = Yes, then do nothing) Amount = 100 Account Bal = 100 Amount = 100 Account Bal = 0 Account Bal = 0
- 21. KAFKA SUMMIT 2023 21 Decouple Services Event Carried State Transfer § Enrich the events with State § Services receive all the details for calc from events § Reduced callbacks between services § Scalable/reduced bottlenecks § Improved resilience – Services function independently, even when other services are briefly down Where is the state? Service 1 Service 6 Service 5 Service 4 Service 2 Service 3 Service 1 Service 6 Service 5 Service 4 Service 2 Service 3 .. .. .. .. .. .. .. .. .. .. .. .. .. ..
- 22. KAFKA SUMMIT 2023 22 Message Retention .. d1 .. d4 .. d1 .. .. dn .. .. .. .. .. .. .. ü Retention Period = 3 days Time Day 4 Day 1 .. d1 .. d4 .. d1 .. d2 .. dn .. d3 .. d6 .. d4 .. d7 .. d5 .. d5 .. d7 Day 1 .. d5 .. d6 .. d6 Arrival Day Day 1 Arrival Day Day 2 Day 3 Expires on Day 4
- 23. KAFKA SUMMIT 2023 23 .. d1 .. d4 .. d1 .. d2 .. dn .. d3 .. d6 .. d4 .. d7 .. d5 .. d5 .. d7 .. d5 .. d6 .. d6 Day 1 Arrival Day Day 2 Day 3 Event Regeneration Service Data Store Day 4 .. d4 .. dn Message Retention Event Regeneration
- 24. KAFKA SUMMIT 2023 24 Account Balance Service Account Balance Service Service 1 Account Balance Service Account Balance Service Service 2 Account Balance Service Account Balance Service Service N Kafka Cluster Topic 1 Topic 2 Topic N Web Socket Read Model <Cache> Ingest data <K, V(t2)> <K, V(t3)> <K, V(t1)> <K, V(t)> <K, V(t)> Last Point in time Value User Views Real-time & historical views Long term Storage API
- 25. KAFKA SUMMIT 2023 25 Summary Takeaways q Managing consistency and state across distributed systems is critical q Kafka streamlines the distribution of large volumes of data between services in near real-time q Kafka streams help in building scalable microservices and to implement scalable distributed event-based architectures q Blending near term and long-term data infrastructure with Kafka provides the ability to build interactive stateful user views
- 26. KAFKA SUMMIT 2023 26 Questions?