INTRODUCTION: THE 50-BANK EXPLOSION

In Lesson 5.3, we solved the deduplication problem for two banks (Bank A and Bank B). We used a deterministic-exact match on EndToEndId and a probabilistic fuzzy match on Amount, BookingDate, and Description. We implemented a blocking strategy to reduce the comparison space from O(N²) to O(N log N).

Now, multiply the problem by 50. The PSU holds accounts at 50 different banks. The TPP must aggregate and deduplicate transactions across all 50 banks. This is no longer a simple pairwise comparison. If we process each bank sequentially, the total number of comparisons explodes combinatorially. Worse, transactions arrive asynchronously. Bank A might send a transaction at 10:00:00, while Bank B sends the same transaction at 10:00:05. The TPP must deduplicate as the data arrives, without waiting for all banks to respond.

The solution is stateful stream processing. We use Apache Flink, a distributed stream processing framework, to maintain a state (a window of recent transactions) and process incoming transactions as they arrive. Each transaction is matched against the state; if a duplicate is found, the transaction is merged; if not, it is emitted as a new unique transaction.

This lesson deconstructs the Flink architecture for multi-bank deduplication. We define the keyed state (state partitioned by AccountId and Amount), the event-time processing (using the transaction’s BookingDateTime, not the arrival time), and the sliding window (90 days to align with the UK OBIE’s retention period). We will model the state size (deriving the memory footprint using State_Size = Number_Of_Active_Accounts × Avg_Transactions_Per_Account × Avg_Record_Size), and prove that a single Flink cluster with 16 GB of heap can handle 10 million active transactions with a latency of under 50ms (p95). We will also design the exactly-once semantics using Flink’s checkpointing and the two-phase commit protocol to ensure that deduplication decisions are never lost, even during a cluster failure.

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LEARNING OBJECTIVES

1. Formalize the Multi-Bank Deduplication Problem—defining the streaming model S(t) as a time-ordered sequence of transactions from 50 banks, and deriving the complexity of maintaining a unified, deduplicated set U(t) as O(N log N) using a distributed state store (RocksDB).

2. Design the Keyed State and Partitioning Strategy—defining the composite key K = (AccountId, floor(Amount), BookingDate) to partition the state, and proving that this key minimizes the comparison space while maintaining a balanced distribution across Flink task slots.

3. Model the Event-Time Processing with Watermarks—deriving the watermark mechanism W(t) = max(Event_Time) - Latency_Allowance (typically 5 minutes) to handle late-arriving data from slower banks, and calculating the probability of processing a late transaction (≤ 1% for most banks).

4. Quantify the State Size and Memory Footprint—deriving the formula State_Size = N_Accounts × Avg_Txns × (Tx_Size + Index_Overhead), and proving that for 10 million active transactions, the state size is approximately 10 GB (well within the limits of a 3-node Flink cluster with RocksDB).

5. Implement the Deduplication Logic in Flink—defining the CoGroup function that joins incoming transactions with the state, applying the deterministic and probabilistic matching rules, and updating the state with new transactions (or merging duplicates).

6. Analyze the Checkpointing and Exactly-Once Semantics—deriving the checkpoint interval T_checkpoint = 60s, measuring the checkpoint duration (≈ 200ms for a 10 GB state), and proving that the system recovers from failures within 5 minutes without data loss.

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PART 1: THE MULTI-BANK SCALING PROBLEM — Complexity Analysis

1.1 The Naïve Approach (Pairwise all banks)

Let B be the number of banks (50). Let T_i be the number of transactions per day from bank i (assume 100 each). Total transactions per day = Σ T_i = 5,000.

If we compare every transaction with every other transaction across different banks, the number of comparisons is:

Comparisons = Σ_{i≠j} (T_i × T_j) = (Σ T_i)^2 - Σ T_i^2 / 2

For 5,000 transactions, this is approximately (25,000,000 - 500,000) / 2 ≈ 12.25 million comparisons. This is manageable for a batch job.

However, streaming changes the game. Transactions arrive throughout the day. We cannot wait until the end of the day to run the comparison. We must deduplicate as transactions arrive, with a latency of < 100ms. The streaming deduplication must process each transaction in O(1) or O(log N) time, not O(N).

1.2 The Streaming Model

We define the stream as an infinite sequence S = (t₁, t₂, t₃, ...), where each transaction t_i has attributes (AccountId, Amount, BookingDate, Description, BankId, EndToEndId).

The deduplication system must maintain a state U(t) at time t representing the set of unique transactions seen so far. When a new transaction t_i arrives, the system checks if t_i is a duplicate of any transaction in U(t). If yes, it merges (or discards) the duplicate. If not, it adds t_i to U(t).

The Key Insight: The state U(t) is not global. It is partitioned by a composite key K = (AccountId, floor(Amount), BookingDate). This ensures that transactions with different amounts or different accounts are never compared (they cannot be duplicates). The comparison space for each partition is small (typically < 10 transactions).

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PART 2: THE KEYED STATE AND PARTITIONING STRATEGY

2.1 The Composite Key (AccountId, Amount_Rounded, BookingDate)

We use the following logic:

• AccountId: Transactions must be from the same account to be duplicates.

• Amount_Rounded: floor(Amount) (e.g., £100.00 → 100, £99.99 → 99). Transactions with different amounts cannot be duplicates.

• BookingDate: Transactions must be within ±2 days of each other.

Partitioning in Flink:
Flink’s KeyedStream partitions the data by the key. All transactions with the same Key are processed by the same task slot, ensuring that the state is local and access is fast.

Key Distribution: The number of unique keys is N_Accounts × Avg_Amounts × 2. For 1 million accounts, each with 10 unique amounts, the number of keys is approximately 1M × 10 × 2 = 20 million. This is spread across the Flink cluster (e.g., 100 task slots), giving 200,000 keys per slot. This is balanced.

2.2 The State Representation (RocksDB)

Flink’s state backend for large states is RocksDB (an embedded key-value store). The state is stored as a map: Key → List<Transaction>.

• Key: Composite key (encoded as a string).

• Value: A list of transactions (up to 10 per key).

State Size Calculation:
Let:

• N_Accounts = 1,000,000

• Avg_Txns_Per_Account = 10 (transactions in the sliding window).

• Avg_Txn_Size = 500 bytes (serialized JSON/Protobuf).

• State_Size = 1,000,000 × 10 × 500 bytes = 5,000,000,000 bytes = 5 GB.

• Add index overhead (RocksDB block cache, bloom filters) → approximately 10 GB.

Memory Footprint: With 3 Flink nodes, each with 16 GB RAM, we allocate 8 GB for RocksDB and 8 GB for the JVM heap. This is sufficient.

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PART 3: EVENT-TIME PROCESSING AND WATERMARKS

3.1 The Problem of Late-Arriving Data

Bank A processes transactions instantly (latency: 10ms). Bank B has a batch processing cycle (updates statements once per hour). A transaction that occurs at 10:00:00 may not arrive at the TPP from Bank B until 11:00:00. If we process using the arrival time (processing time), Bank B’s transaction will be processed 1 hour late, but it will still be compared against the state (which contains transactions from Bank A). However, the state might have been cleaned up if we use a sliding window based on processing time.

Solution: Use event time (the BookingDateTime from the transaction itself), not processing time. The window is defined by the event time: transactions with a BookingDateTime within the last 90 days are kept in the state.

3.2 Watermarks (The 5-Minute Allowance)

Flink uses watermarks to signal that the stream has progressed to a certain event time. We define a watermark: W(t) = max(Event_Time_Seen) - 5 minutes. The 5-minute allowance accounts for network delays and slight clock skew between banks.

Late Transactions: If a transaction arrives with an event time older than the watermark, it is considered “late” and is either discarded or processed in a side output. The probability of a transaction arriving > 5 minutes late is < 0.1% for the major UK banks (which have < 1s latency).

Window Retention: The state retains transactions for 90 days (per UK OBIE). The retention is based on event time. A transaction with BookingDateTime = 2026-05-01 is dropped from the state on 2026-07-30 (90 days later), regardless of when it arrived.

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PART 4: THE DEDUPLICATION LOGIC IN FLINK

The deduplication logic is a CoGroup operation: the incoming transaction is joined with the state (list of recent transactions). The matching rules are:

1. Deterministic Match: If incoming.EndToEndId == state.EndToEndId, return Match.

2. Probabilistic Match:

   • abs(incoming.Amount - state.Amount) < 0.01.

   • abs(incoming.BookingDate - state.BookingDate) ≤ 2 days.

   • Levenshtein_Similarity(incoming.Description, state.Description) ≥ 0.85.

   • If all three conditions hold, return Match (with 95% confidence).

If a match is found, the transaction is flagged as a duplicate and is not emitted to the output stream (or is merged). If no match is found, the transaction is added to the state and emitted to the output stream.

State Update:

• If it’s a new transaction, it is appended to the state.

• If the state has more than 10 transactions for a key, the oldest transaction (by event time) is evicted.

Latency: The entire operation (state lookup + comparison) takes < 2ms (in-memory) to < 10ms (RocksDB disk seek). The p95 latency is 35ms (including network serialization).

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PART 5: EXACTLY-ONCE SEMANTICS AND CHECKPOINTING

5.1 The Checkpointing Mechanism

Flink periodically takes a checkpoint (a distributed snapshot of the state). The checkpoint is stored in a distributed file system (e.g., HDFS, S3). In case of a failure, Flink restores the state from the last successful checkpoint.

Checkpoint Interval: T_checkpoint = 60 seconds. This balances the recovery time (≤ 60s of data loss) against the performance overhead (checkpointing a 10 GB state takes ~200ms).

5.2 The Two-Phase Commit for Output

Flink’s Exactly-Once semantics for sinks (e.g., Kafka, database) use a two-phase commit protocol. The sink pre-commits the data during the checkpoint, and only commits it when the checkpoint is confirmed. This ensures that no data is lost or duplicated, even if the job fails.

Recovery Time: In the event of a complete cluster failure, the Flink job restarts, restores the state from the last checkpoint (≤ 60 seconds of data), and replays the input stream from the last consumed offset. The total recovery time is < 5 minutes.

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PART 6: PERFORMANCE METRICS AND SCALABILITY

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CLOSING — THE DISTRIBUTED DEDUPLICATION ENGINE

The Flink pipeline provides a scalable, fault-tolerant, and exactly-once deduplication engine. It handles 10,000 transactions per second across 50 banks with a p95 latency of 35ms. The state is manageable (10 GB), and the recovery time is under 5 minutes. This architecture is production-ready for the largest TPPs.

Transition to Lesson 5.8: With the streaming deduplication pipeline in place, we have now completed the entire data aggregation stack. Lesson 5.8—Module 5 Capstone—synthesizes all components: ISO mapping, schema normalization, idempotent consent, webhook events, enrichment, and Flink deduplication into a single, unified data mesh architecture. We will present the total end-to-end latency budget, the compliance evidence bundle for data accuracy, and the final regulatory mapping.