analyticsproductmarketplaces

How Marketplaces Can Use ClickHouse to Power Real-Time Deal Scanners

UUnknown

2026-02-17

11 min read

How ClickHouse’s OLAP strengths help marketplaces power real-time deal scanners and fast product-launch landing pages in 2026.

Hook — You need buyers to see fresh deals the moment they appear

Marketplaces and directories live or die on two things: speed and relevance. Buyers expect landing pages and deal scanners to surface the best product launches, discounts, and vendor opportunities in real time — not minutes or hours later. Yet many teams are crushed by slow analytics, stale caches, and complex pipelines that can’t produce fast, accurate top-N lists per buyer segment. For local-focused marketplaces — whether you cover micro-events or small pop-up retail — playbooks like Small‑City Night Markets 2026: A Local Newsroom Playbook show how discoverability and local sequencing matter on the front end.

Executive summary — Why ClickHouse matters for real-time deal scanners in 2026

ClickHouse’s OLAP engine combines high-throughput ingestion, low-latency analytical queries, and cost-effective storage — making it a strong choice for powering live deal scanners and product-launch landing pages. In late 2025 ClickHouse raised a large growth round (reported by Bloomberg), signaling broad adoption and rapid product development. For marketplaces building buyer-facing experiences, ClickHouse enables:

Millisecond-to-second query latency for top-N and aggregation queries across millions of events.
High ingest throughput via Kafka/HTTP connectors and materialized views to pre-aggregate streams (see patterns from Cloud pipelines case studies for pipeline scaling).
Cost efficiency vs running transactional systems for analytics — less compute for high-concurrency reads.
Operational patterns for keeping landing pages fresh: precompute, cache, and edge-distribute results.

ClickHouse’s growth and recent funding (reported by Dina Bass/Bloomberg) accelerated platform improvements in late 2025 — more cloud automation, tighter Kafka integrations, and production-ready cluster features that marketplaces can leverage in 2026.

How marketplaces typically break when they try to be “real-time”

Most failures come from three places:

Trying to run analytical joins and heavy aggregations at request time (high tail latency).
Relying on slow ETL jobs (batch windows of 5–60 minutes) that produce stale landing pages.
Not precomputing buyer-specific top-N views, causing duplicated work across tens of thousands of concurrent users.

The remedy is an architecture that separates fast reads from heavy computation and keeps precomputed feeds fresh as events arrive. Many teams pair ClickHouse precomputation with lightweight personalization layers (see AI‑powered discovery & personalization) to avoid per-user heavy queries.

Core architecture: ClickHouse-based real-time deal scanner (high-level)

Below is a practical pipeline to surface deals in real time, optimized for landing pages and deal scanners.

Ingest: Events (new launches, price changes, seller signals) flow into Kafka or ClickHouse HTTP API. If you’re building resilient ingestion pipelines, studying cloud pipeline case studies helps — see cloud pipeline scaling examples.
Enrich & Score: A lightweight stream processor enriches events (category, geo, tag normalization) and computes scores — tag-driven approaches are discussed in Tag‑Driven Commerce.
Store raw events in a ClickHouse MergeTree table optimized for time-series ingest.
Materialized views / pre-aggregations compute per-segment top-N, rolling metrics, and TTL-based pruning. For teams using object and cold storage strategies, review object storage analyses like Top Object Storage Providers for AI Workloads when planning retention and tiering.
API / Edge cache serves results: read the precomputed top lists, push to Redis or a CDN/edge orchestration layer for sub-100ms first-byte times on landing pages.

Why ClickHouse fits each layer

Ingest: Kafka/ClickHouse engine supports millions of rows/sec and is battle-tested in 2025/2026 production workloads — combine this with robust cloud pipeline patterns (cloud pipelines).
Pre-aggregation: Materialized views and AggregatingMergeTree tables let you compute summaries on insert. Teams building creator-facing marketplaces often tie pre-aggregation to discovery systems as described in AI personalization playbooks.
Fast reads: Columnar storage + vectorized execution gives very fast GROUP BY/top-N queries across large datasets.
Cost/perf: Pay-for-compute vs pay-for-storage tradeoffs are favorable compared to running heavyweight OLTP queries for analytics. If you need managed autoscaling and fewer ops responsibilities, look at managed cloud offerings and pipeline automation examples (cloud pipeline case study).

Practical ClickHouse schema patterns for deal scanners

Designing tables correctly is where most performance is won or lost. Below are patterns we use in production for marketplaces.

1) Raw events table (append-only)

CREATE TABLE events_raw (
  event_time DateTime64(3),
  product_id UInt64,
  seller_id UInt64,
  event_type String, -- launch|price_change|stock
  price Float64,
  old_price Float64,
  category_id UInt32,
  tags Array(String),
  attributes Nested(...),
  payload String
) ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/events_raw', '{replica}')
ORDER BY (category_id, toYYYYMM(event_time), product_id)
TTL event_time + INTERVAL 90 DAY
SETTINGS index_granularity = 8192;

Key notes: order by category then time for efficient per-category scans; TTL keeps the table bounded.

2) Aggregated top-N per segment

For landing pages you want precomputed top lists per category, country, or buyer-cohort.

CREATE MATERIALIZED VIEW mv_top_by_category
TO top_by_category
AS
SELECT
  category_id,
  product_id,
  argMax(price, event_time) AS price,
  count(*) AS events_count,
  max(discount_pct) AS max_discount,
  now() AS last_updated,
  sum(score) AS score
FROM events_raw
GROUP BY category_id, product_id

CREATE TABLE top_by_category (
  category_id UInt32,
  product_id UInt64,
  price Float64,
  events_count UInt64,
  max_discount Float64,
  last_updated DateTime64(3),
  score Float64
) ENGINE = AggregatingMergeTree()
ORDER BY (category_id, score) -- pack by category and rank by score

Use a MergeTree variant appropriate for your aggregation style (AggregatingMergeTree, SummingMergeTree, etc.). If your product catalog ties into an e-commerce storefront, patterns from building scalable product catalogs are useful (Product catalog patterns).

3) Prebuilt top-N view for ultra-fast reads

Serialize the top 50 products per category into a table optimized for single-row reads.

CREATE TABLE top50_category (
  category_id UInt32,
  top_products Array(Tuple(product_id UInt64, score Float64, price Float64, max_discount Float64)),
  last_updated DateTime64(3)
) ENGINE = ReplacingMergeTree()
ORDER BY category_id;

-- Populate via a periodic INSERT or a materialized view that aggregates mv_top_by_category and picks top 50 per category

This lets the API serve one small row per category and keep SSD and memory usage tiny. Put this behind Redis or your CDN for instant landing page rendering — pairing ClickHouse with an edge orchestration layer improves latency and security (edge orchestration).

Streaming patterns: keep it real-time without straining ClickHouse

Directly querying raw tables for live scoring is costly. These patterns keep clickhouse healthy while delivering live freshness:

Kafka→ClickHouse engine: Use the Kafka table engine + materialized view consumer to atomically insert enriched events into MergeTree tables; combine with proven cloud pipeline designs for reliability.
Micro-batching: Materialized views process batches; adjust batch size to balance latency vs throughput.
Two-tier stores: Keep a tiny Redis cache with fresh results for 1–10 seconds and rely on ClickHouse for 1–60 minute windows. If you prefer a serverless/edge approach for those micro-caches, see strategies for serverless edge.
Approximate prefilters: Use simple filters or Bloom filters at the edge to reduce downstream QPS for low-interest queries.

Query patterns you’ll use frequently

Top 10 newest launches in a category (sub-second)

SELECT product_id, price, max_discount, last_updated
FROM top50_category
WHERE category_id = 123
ORDER BY arrayElement(top_products, 1).2 DESC
LIMIT 10;

Because the heavy work has already been done, this becomes a tiny, focused read that ClickHouse (or Redis) returns immediately.

Rolling metrics for UI badges (e.g., "5 new deals in 1h")

SELECT category_id, countIf(event_type='launch' AND event_time >= now() - INTERVAL 1 HOUR) AS new_launches
FROM events_raw
WHERE category_id IN (123, 456)
GROUP BY category_id;

These queries use ClickHouse’s vectorized execution and perform across millions of rows fast — but keep them pre-aggregated if you need them for every web request. For personalized scoring and hybrid approaches (precompute then re-rank), see work on AI‑powered personalization and tag-driven segmentation (Tag‑Driven Commerce).

Operational best practices (latency, scaling, cost)

Sharding & replication: Use sharded clusters for write throughput and replicas for high availability. ClickHouse’s native replication is mature in 2026.
Projections & data skipping: Use projections to store sorted pre-materialized data and tokenbf_v1 / minmax indexes to reduce IO for selective queries. When planning retention, consult object-storage and cold-tier guides (object storage reviews).
Compression codecs: Pick LZ4 for speed on hot segments; ZSTD for cold storage. Tune by measuring CPU vs IO tradeoffs.
Monitoring: Track ingestion lag, merge queue length, query latency P50/P95/P99, and disk pressure. Set alerts before merges saturate IO. Use local testing tools and hosted tunnels to validate rollouts (hosted tunnels & local testing).
Test with production traffic: Replay real traffic and synthetic spikes. ClickHouse behaves differently under high-concurrency analytical reads vs single-threaded OLTP. Pipelines and replay tooling from cloud pipeline case studies can help (cloud pipeline examples).

Front-end strategies: render fast landing pages

The fastest UIs avoid querying the analytics layer on each page load. Use a combination of methods:

Edge-cached precomputed endpoints: Push top50_category rows to a CDN endpoint refreshed every few seconds — serverless edge approaches and orchestration patterns are covered in serverless edge and edge orchestration guides.
Server-side rendering (SSR): Fetch the small precomputed row and render HTML on the edge for SEO and first contentful paint.
Adaptive freshness: For high-traffic, high-urgency categories (e.g., product launches), refresh every 1–2 seconds. For long-tail categories, 10–60 seconds is often acceptable and saves cost.
Client fallbacks: Show cached content and display a ‘Live’ badge that updates via WebSocket/Server-sent events when a newer top-N becomes available.

Real-world example: launching a “New Product Scanner” in 7 days

Here’s a pragmatic roadmap that several marketplaces have used to build a buyer-facing deal scanner in under a week. This plan assumes you have ClickHouse (cloud or self-hosted), an event source, and a basic front-end stack.

Day 1: Define the event contract (launch, price_change, stock) and schema. Start a Kafka topic or HTTP collector.
Day 2: Create events_raw MergeTree and a consumer materialized view for enrichment (category mapping, scoring).
Day 3: Implement a materialized view to populate top_by_category and a job to generate top50_category.
Day 4: Build an API route that reads top50_category and returns JSON optimized for SSR. Add Redis as an optional micro-cache.
Day 5: Wire the API to an edge CDN and implement SSR landing pages. Add Live indicators for items updated in the last 30s.
Day 6: Load test with synthetic traffic (ramp to expected QPS). Tune MergeTree index_granularity and projection settings.
Day 7: Monitor and iterate — adjust TTLs, refresh intervals, and top-N thresholds based on buyer behavior and cost.

Scoring & personalization at scale

Marketplaces often want personalized deal lists. Two scalable approaches:

Segmented precomputation — compute top lists for popular segments (e.g., location, buyer cohort, category). This scales linearly with segment count and is simple to serve; similar segmentation ideas appear in tag-driven commerce playbooks (Tag‑Driven Commerce).
Hybrid personalization — precompute global top lists in ClickHouse, then apply a lightweight personalization layer (re-ranking by user signals) in the API server or a Redis layer before rendering. For advanced personalization strategies and discovery models, see AI‑powered discovery.

For millions of users, the hybrid model is most cost-effective: ClickHouse handles the heavy aggregations and you do minor re-ranking per request in memory.

Metrics you must track for buyer experience

Freshness latency: time from event ingestion to appearance on the landing page (goal: < 5s for launch-critical categories; < 30s for most).
Time-to-first-byte (TTFB) for landing pages: aim for < 200ms after CDN edge hit. Edge orchestration and serverless edge patterns can help (edge orchestration / serverless edge).
Query P99 latency: ensure precomputed reads remain sub-100ms for user-facing endpoints.
Cache hit ratio: monitor CDN & Redis hit rates — aim >95% for mainstream categories.

Costs and trade-offs vs other OLAP choices

ClickHouse often costs less per query than serverless OLAP or full cloud warehouses for real-time use cases because it’s optimized for high-concurrency, columnar reads. However:

Operational overhead for self-hosted clusters can be higher — evaluate ClickHouse Cloud if you want managed autoscaling (Cloud features accelerated in late 2025). Managed offerings and pipeline automation are covered in cloud pipeline case studies (cloud pipeline case study).
Approximate functions (uniqCombined) are powerful for high-cardinality counts — use them where exactness is not critical to lower resource use.

Recent trends (2025–2026) that change the calculus

Two developments matter for marketplaces:

In late 2025 ClickHouse secured a major funding round, accelerating feature development in its cloud and cluster tooling (reported by Bloomberg). That means improved managed offerings and faster iteration for serverless-like experiences in 2026.
Edge computing and CDN integration improved across the industry in 2025–2026, making it easier to publish precomputed ClickHouse results at the edge for immediate buyer experiences — see practical orchestration guidance in edge orchestration and serverless approaches (serverless edge).

Common pitfalls and how to avoid them

Not precomputing top-N: don’t compute heavy sorts at request time. Build top-N tables and cache them.
Poor ORDER BY choices: a bad primary key causes expensive reads. Order by the dimension you query most (category, segment).
Ignoring merges & disk pressure: monitor merges and keep disk headroom. Merge storms kill tail latency.
Over-personalizing in ClickHouse: avoid computing top-N per individual user; segment-first then re-rank is cheaper and faster. Tag-driven commerce patterns can help balance scale vs relevance (Tag‑Driven Commerce).

Checklist: Launch a ClickHouse-powered deal scanner

Define event contract and scoring model.
Set up Kafka or HTTP ingestion into ClickHouse.
Create raw MergeTree table with TTL and good ORDER BY.
Build materialized views to compute top-by-segment and top50 tables.
Expose a tiny API that reads the precomputed rows and cache at the edge.
Instrument freshness, P99 latency, and cache hit rate.
Iterate on scoring and personalization using live A/B tests.

Final takeaways — Why this matters to marketplace operators

In 2026, buyer expectations for instant, relevant discovery are higher than ever. ClickHouse’s OLAP strengths — fast aggregation, massive ingest, and efficient storage — let marketplaces build deal scanners and product launch landing pages that feel live, accurate, and scalable. The technique is simple: move heavy work off the request path, precompute the things buyers need, cache intelligently at the edge, and keep freshness metrics under strict SLOs. If you run physical micro-events or pop-ups, pair these analytics patterns with hybrid pop-up strategies (Advanced Hybrid Pop‑Ups) and portable seller kits (Portable Live‑Sale Kits).

Get started — concrete next steps

If you’re building or improving a marketplace deal scanner this quarter, do these three things today:

Sketch your event schema and desired buyer segments for top-N lists.
Spin up a ClickHouse instance (or ClickHouse Cloud trial) and create an events_raw table — if you want to reduce ops, review managed cloud pipeline and autoscaling examples (cloud pipelines).
Wire a Kafka / HTTP producer and a quick materialized view that writes top50 rows for one high-value category — measure freshness and P99. For secure edge deployments, check edge orchestration patterns (edge orchestration).

Want a checklist, SQL templates, and a reference repo tuned for marketplaces? Contact us for a starter pack that includes tested ClickHouse schema templates, deployment notes, and front-end cache patterns to get your first real-time landing page live in a week. Learn how tag-driven discovery and product catalogs tie together with commerce playbooks (product catalog patterns).

Call to action

Ready to turn your marketplace into a real-time discovery engine? Request the marketplace ClickHouse starter pack and a 30-minute architecture review from our experts — we’ll help you pick the right ingestion pattern, design pre-aggregations, and ship a fast product launch landing page that buyers actually use.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.