Introduction

An events table grows fast, and duplicate data becomes expensive long before storage is the real problem. It distorts analytics, inflates billing metrics, and turns simple GROUP BY queries into slow, unreliable jobs. At scale, good deduplication starts with clear rules and ends with incremental processing that avoids full-table rescans.

Start With a Precise Definition of Duplicate

The database cannot remove duplicates correctly unless the application defines what a duplicate actually is. Sometimes the answer is a producer-generated event_id. In other systems, the event identity is a composite of fields such as tenant_id, event_type, and a normalized timestamp.

When that rule is vague, different pipelines make different choices. One batch job keeps the earliest row, another keeps the latest, and downstream dashboards drift. Write the rule down and make it deterministic before you write any SQL.

1CREATE TABLE events_raw (
2  event_id VARCHAR(64),
3  tenant_id BIGINT NOT NULL,
4  event_type VARCHAR(64) NOT NULL,
5  event_time TIMESTAMP NOT NULL,
6  payload JSONB NOT NULL,
7  ingested_at TIMESTAMP NOT NULL DEFAULT NOW()
8);

If event_id is not trustworthy, add a derived fingerprint during ingestion so every later step uses the same dedupe key.

Remove Duplicates With Stable Tie-Breaking

A scalable dedupe query should say exactly which row survives and why. Window functions are a good fit because they make the tie-breaking rule visible.

1WITH ranked AS (
2  SELECT
3    tenant_id,
4    event_type,
5    event_time,
6    payload,
7    ingested_at,
8    ROW_NUMBER() OVER (
9      PARTITION BY tenant_id, event_type, event_time, payload
10      ORDER BY ingested_at ASC
11    ) AS rn
12  FROM events_raw
13  WHERE event_time >= DATE '2026-03-01'
14    AND event_time < DATE '2026-03-02'
15)
16SELECT tenant_id, event_type, event_time, payload, ingested_at
17FROM ranked
18WHERE rn = 1;

This query keeps the earliest ingested copy for one partition window. The important design choice is the bounded time filter. Recomputing dedupe across the full history every hour does not scale well and usually provides no business value.

Group From a Cleaned Table, Not From Raw Data

Analytics queries become much simpler when dedupe is a dedicated stage instead of an afterthought stuffed into every report. A common pattern is to write cleaned rows into a second table and build rollups from there.

1INSERT INTO event_counts_daily (day, tenant_id, event_type, total_events)
2SELECT
3  DATE(event_time) AS day,
4  tenant_id,
5  event_type,
6  COUNT(*) AS total_events
7FROM events_deduped
8WHERE event_time >= :window_start
9  AND event_time < :window_end
10GROUP BY DATE(event_time), tenant_id, event_type;

This approach gives you predictable query cost and simpler debugging. If the daily counts are wrong, you can inspect the deduped table directly instead of reverse-engineering nested report logic.

Partition for the Queries You Actually Run

Partitioning helps only when it matches ingestion and query behavior. Event systems usually partition by event date or ingestion date because most reprocessing and reporting happens in recent windows. Indexes should support the dedupe key and the most common grouping dimensions, but not every possible filter. Over-indexing hurts write throughput and creates maintenance work with little payoff.

Late-arriving events need their own policy. If a mobile client retries an event six hours later, your system needs a way to correct the relevant partition without touching the rest of the dataset. Many teams keep recent partitions mutable for a short window and mark older partitions as finalized.

Use Reconciliation Metrics to Catch Drift

A dedupe pipeline is not finished when the SQL works once. You also need evidence that it keeps working as traffic patterns change. Useful operational checks include duplicate rate by source, the count of dropped rows per partition, and a comparison between raw and deduped totals for known windows.

If those metrics spike, the cause is often upstream. A producer may have started sending duplicate IDs, a hashing rule may have changed, or late-arrival volume may have increased enough to break old assumptions. Monitoring gives you a chance to fix the pipeline before business users notice broken charts.

Common Pitfalls

The biggest mistake is treating dedupe as a cosmetic cleanup instead of a data contract. Teams also run into trouble when tie-breaking rules are implicit, when grouping is done against raw data, or when every job rescans the full table. Another common problem is finalizing partitions too early and losing the ability to correct late events cleanly.

Summary

• Define duplicate identity explicitly before building the pipeline.
• Use stable tie-breaking rules so dedupe results are repeatable.
• Separate raw ingestion, deduped storage, and grouped aggregates.
• Process bounded windows instead of full-history recomputes.
• Track reconciliation metrics so drift is visible before reports break.